The development of a custom algorithm begins with a clear understanding of the epidemiological question at hand. This involves defining the objectives and scope of the study. Data collection follows, where researchers gather relevant data, ensuring it is clean and well-structured. The next step involves selecting appropriate models or techniques, which could range from machine learning algorithms to statistical models. Finally, the algorithm is validated using historical data or through simulations to test its predictive capability and accuracy.
